Decoding molecular signatures and identifying therapeutic targets in triple-negative breast cancer subtypes using omics and network analysis

Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer. It is resistant to therapeutic applications, making it very difficult to manage. Most patients continue to receive chemotherapy. The main challenges in treating TNBC arise from its histopathological, transcriptomic, and genomic heterogeneity. While subtype classification was once essential for understanding TNBC's diverse behavior, it is now crucial to thoroughly evaluate the molecular characteristics of each subtype to improve TNBC management. Our study employed a multi-omics approach utilizing transcriptomic data to compare TNBC subtype-specific cell lines with normal breast epithelial cell lines. Our goal was to find out essential transcription factors (TFs) regulating cancer-related pathways, subtype-specific marker genes, and their key TFs. We identified a common set of genes involved in cancer related pathways and examined their alteration level with driver mutations in each subtype. Our findings indicate that, based on alteration patterns, survivability, differential gene and protein level expressions, the genes ERBB3, ERBB4, HLA-B, AKT1, GNAS, FGFR4, PIK3R3 and AXIN2 emerge as primary risk factors in TNBC. These genes, associated with specific subtypes, are potential markers and should be the focus of further studies, paving the way for personalized anticancer treatment strategies for TNBC patients.

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Publication Details

Journal
Scientific Reports
Published
2025-12-24
DOI
https://doi.org/10.1038/s41598-025-28191-3
Primary Topic
Breast Cancer Treatment Studies
Type
article
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article

Decoding molecular signatures and identifying therapeutic targets in triple-negative breast cancer subtypes using omics and network analysis

Chittabrata Mal, Abdullah M. Alshahrani, Soumya K. Kar, Subrata Mondal et al.
Scientific Reports
Breast Cancer Treatment Studies
article

Decoding molecular signatures and identifying therapeutic targets in triple-negative breast cancer subtypes using omics and network analysis

Chittabrata Mal, Abdullah M. Alshahrani, Soumya K. Kar, Subrata Mondal, Debapriya Sanyal, S. Rehan Ahmad
article en

Abstract

Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer. It is resistant to therapeutic applications, making it very difficult to manage. Most patients continue to receive chemotherapy. The main challenges in treating TNBC arise from its histopathological, transcriptomic, and genomic heterogeneity. While subtype classification was once essential for understanding TNBC's diverse behavior, it is now crucial to thoroughly evaluate the molecular characteristics of each subtype to improve TNBC management. Our study employed a multi-omics approach utilizing transcriptomic data to compare TNBC subtype-specific cell lines with normal breast epithelial cell lines. Our goal was to find out essential transcription factors (TFs) regulating cancer-related pathways, subtype-specific marker genes, and their key TFs. We identified a common set of genes involved in cancer related pathways and examined their alteration level with driver mutations in each subtype. Our findings indicate that, based on alteration patterns, survivability, differential gene and protein level expressions, the genes ERBB3, ERBB4, HLA-B, AKT1, GNAS, FGFR4, PIK3R3 and AXIN2 emerge as primary risk factors in TNBC. These genes, associated with specific subtypes, are potential markers and should be the focus of further studies, paving the way for personalized anticancer treatment strategies for TNBC patients.

Scientific ReportsVol. 15(1)
University of Burdwan (IN), Government of West Bengal (IN), King Khalid University (SA), Maulana Abul Kalam Azad University of Technology, West Bengal (IN)
Good health and well-being
Openalex Percentile: Top 34%
Breast Cancer Treatment Studies
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Decoding molecular signatures and identifying therapeutic targets in triple-negative breast cancer subtypes using omics and network analysis — Chittabrata Mal, Abdullah M. Alshahrani, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS